Pandas Profiling Report

Dataset statistics

Number of variables	14
Number of observations	500
Missing cells	18
Missing cells (%)	0.3%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	58.6 KiB
Average record size in memory	120.0 B

Variable types

Categorical	5
Numeric	9

Warnings

`Name` has a high cardinality: 465 distinct values	High cardinality
`Club` has a high cardinality: 98 distinct values	High cardinality
`Nation` has a high cardinality: 58 distinct values	High cardinality
`League` is highly correlated with `Club`	High correlation
`Club` is highly correlated with `League`	High correlation
`Name` is uniformly distributed	Uniform

Reproduction

Analysis started	2021-03-30 14:46:18.053258
Analysis finished	2021-03-30 14:46:27.528387
Duration	9.48 seconds
Software version	pandas-profiling v2.11.0
Download configuration	config.yaml

Name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	465
Distinct (%)	93.0%
Missing	0
Missing (%)	0.0%
Memory size	7.8 KiB

Daniele Rugani	3
Márcio Rafael Ferreira de Souza	2
Diego Perotti	2
Felipe Anderson Pereira Gomes	2
Rúben Santos Gato Alves Dias	2
Other values (460)	489

Length

Max length	35
Median length	15
Mean length	17.152
Min length	8

Characters and Unicode

Total characters	8576
Distinct characters	92
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	431 ?
Unique (%)	86.2%

Sample

1st row	Lionel Messi
2nd row	C. Ronaldo dos Santos Aveiro
3rd row	Neymar da Silva Santos Jr.
4th row	Kevin De Bruyne
5th row	Robert Lewandowski

Value	Count	Frequency (%)
Daniele Rugani	3	0.6%
Márcio Rafael Ferreira de Souza	2	0.4%
Diego Perotti	2	0.4%
Felipe Anderson Pereira Gomes	2	0.4%
Rúben Santos Gato Alves Dias	2	0.4%
Morgan Sanson	2	0.4%
Alejandro Gómez	2	0.4%
Douglas Costa de Souza	2	0.4%
Arkadiusz Milik	2	0.4%
Gonzalo Higuaín	2	0.4%
Other values (455)	479	95.8%

Histogram of lengths of the category

Value	Count	Frequency (%)
de	23	1.8%
silva	19	1.5%
da	13	1.0%
josé	12	0.9%
santos	10	0.8%
lucas	9	0.7%
souza	7	0.5%
sergio	7	0.5%
garcía	7	0.5%
dos	6	0.5%
Other values (861)	1169	91.2%

Most occurring characters

Value	Count	Frequency (%)
a	885	10.3%
	782	9.1%
e	688	8.0%
r	581	6.8%
o	580	6.8%
i	579	6.8%
n	488	5.7%
l	391	4.6%
s	344	4.0%
d	230	2.7%
Other values (82)	3028	35.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6533	76.2%
Uppercase Letter	1246	14.5%
Space Separator	782	9.1%
Other Punctuation	8	0.1%
Dash Punctuation	7	0.1%

Most frequent character per category

Value	Count	Frequency (%)
a	885	13.5%
e	688	10.5%
r	581	8.9%
o	580	8.9%
i	579	8.9%
n	488	7.5%
l	391	6.0%
s	344	5.3%
d	230	3.5%
u	229	3.5%
Other values (43)	1538	23.5%

Value	Count	Frequency (%)
M	140	11.2%
S	110	8.8%
A	104	8.3%
J	74	5.9%
R	71	5.7%
G	68	5.5%
L	67	5.4%
D	67	5.4%
C	64	5.1%
B	62	5.0%
Other values (25)	419	33.6%

Value	Count	Frequency (%)
.	7	87.5%
'	1	12.5%

Value	Count	Frequency (%)
	782	100.0%

Value	Count	Frequency (%)
-	7	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7779	90.7%
Common	797	9.3%

Most frequent character per script

Value	Count	Frequency (%)
a	885	11.4%
e	688	8.8%
r	581	7.5%
o	580	7.5%
i	579	7.4%
n	488	6.3%
l	391	5.0%
s	344	4.4%
d	230	3.0%
u	229	2.9%
Other values (78)	2784	35.8%

Value	Count	Frequency (%)
	782	98.1%
.	7	0.9%
-	7	0.9%
'	1	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8301	96.8%
None	275	3.2%

Most frequent character per block

Value	Count	Frequency (%)
a	885	10.7%
	782	9.4%
e	688	8.3%
r	581	7.0%
o	580	7.0%
i	579	7.0%
n	488	5.9%
l	391	4.7%
s	344	4.1%
d	230	2.8%
Other values (46)	2753	33.2%

Value	Count	Frequency (%)
é	66	24.0%
í	43	15.6%
á	41	14.9%
ć	23	8.4%
ú	13	4.7%
ó	11	4.0%
ñ	9	3.3%
ã	9	3.3%
ü	7	2.5%
Á	6	2.2%
Other values (26)	47	17.1%

Club
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct	98
Distinct (%)	19.6%
Missing	0
Missing (%)	0.0%
Memory size	7.8 KiB

Atlético de Madrid	22
Real Madrid	20
Piemonte Calcio	18
Manchester City	17
Tottenham Hotspur	17
Other values (93)	406

Length

Max length	27
Median length	13
Mean length	13.004
Min length	3

Characters and Unicode

Total characters	6502
Distinct characters	64
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	33 ?
Unique (%)	6.6%

Sample

1st row	FC Barcelona
2nd row	Piemonte Calcio
3rd row	Paris Saint-Germain
4th row	Manchester City
5th row	Bayern München

Value	Count	Frequency (%)
Atlético de Madrid	22	4.4%
Real Madrid	20	4.0%
Piemonte Calcio	18	3.6%
Manchester City	17	3.4%
Tottenham Hotspur	17	3.4%
FC Barcelona	16	3.2%
Bayern München	16	3.2%
Inter	16	3.2%
Liverpool	15	3.0%
Chelsea	15	3.0%
Other values (88)	328	65.6%

Histogram of lengths of the category

Value	Count	Frequency (%)
madrid	42	4.4%
fc	38	3.9%
de	37	3.8%
real	36	3.7%
manchester	31	3.2%
city	26	2.7%
united	23	2.4%
atlético	22	2.3%
cf	20	2.1%
borussia	20	2.1%
Other values (152)	668	69.4%

Most occurring characters

Value	Count	Frequency (%)
e	647	10.0%
a	602	9.3%
	463	7.1%
i	461	7.1%
n	382	5.9%
r	381	5.9%
t	371	5.7%
l	332	5.1%
o	329	5.1%
s	223	3.4%
Other values (54)	2311	35.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	4930	75.8%
Uppercase Letter	1055	16.2%
Space Separator	463	7.1%
Decimal Number	27	0.4%
Other Punctuation	14	0.2%
Dash Punctuation	13	0.2%

Most frequent character per category

Value	Count	Frequency (%)
e	647	13.1%
a	602	12.2%
i	461	9.4%
n	382	7.7%
r	381	7.7%
t	371	7.5%
l	332	6.7%
o	329	6.7%
s	223	4.5%
d	206	4.2%
Other values (22)	996	20.2%

Value	Count	Frequency (%)
C	151	14.3%
M	114	10.8%
B	112	10.6%
S	82	7.8%
L	75	7.1%
F	73	6.9%
A	70	6.6%
R	56	5.3%
P	52	4.9%
G	47	4.5%
Other values (12)	223	21.1%

Value	Count	Frequency (%)
0	9	33.3%
4	9	33.3%
9	4	14.8%
1	3	11.1%
8	2	7.4%

Value	Count	Frequency (%)
'	7	50.0%
.	5	35.7%
&	2	14.3%

Value	Count	Frequency (%)
	463	100.0%

Value	Count	Frequency (%)
-	13	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	5985	92.0%
Common	517	8.0%

Most frequent character per script

Value	Count	Frequency (%)
e	647	10.8%
a	602	10.1%
i	461	7.7%
n	382	6.4%
r	381	6.4%
t	371	6.2%
l	332	5.5%
o	329	5.5%
s	223	3.7%
d	206	3.4%
Other values (44)	2051	34.3%

Value	Count	Frequency (%)
	463	89.6%
-	13	2.5%
0	9	1.7%
4	9	1.7%
'	7	1.4%
.	5	1.0%
9	4	0.8%
1	3	0.6%
8	2	0.4%
&	2	0.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6446	99.1%
None	56	0.9%

Most frequent character per block

Value	Count	Frequency (%)
e	647	10.0%
a	602	9.3%
	463	7.2%
i	461	7.2%
n	382	5.9%
r	381	5.9%
t	371	5.8%
l	332	5.2%
o	329	5.1%
s	223	3.5%
Other values (48)	2255	35.0%

Value	Count	Frequency (%)
é	29	51.8%
ü	16	28.6%
ş	6	10.7%
ç	3	5.4%
ó	1	1.8%
ã	1	1.8%

League
Categorical

HIGH CORRELATION

Distinct	19
Distinct (%)	3.8%
Missing	0
Missing (%)	0.0%
Memory size	7.8 KiB

England Premier League	125
Spain Primera División	115
Italy Serie A	81
Germany 1. Bundesliga	65
France Ligue 1	30
Other values (14)	84

Length

Max length	26
Median length	22
Mean length	19.452
Min length	13

Characters and Unicode

Total characters	9726
Distinct characters	48
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4 ?
Unique (%)	0.8%

Sample

1st row	Spain Primera División
2nd row	Italy Serie A
3rd row	France Ligue 1
4th row	England Premier League
5th row	Germany 1. Bundesliga

Value	Count	Frequency (%)
England Premier League	125	25.0%
Spain Primera División	115	23.0%
Italy Serie A	81	16.2%
Germany 1. Bundesliga	65	13.0%
France Ligue 1	30	6.0%
Portugal Primeira Liga	20	4.0%
China Super League	15	3.0%
Holland Eredivisie	8	1.6%
Turkey Süper Lig	8	1.6%
Ukraine Premier Liga	6	1.2%
Other values (9)	27	5.4%

Histogram of lengths of the category

Value	Count	Frequency (%)
league	155	10.4%
premier	131	8.8%
england	126	8.5%
división	116	7.8%
primera	116	7.8%
spain	115	7.7%
1	95	6.4%
italy	81	5.4%
a	81	5.4%
serie	81	5.4%
Other values (32)	390	26.2%

Most occurring characters

Value	Count	Frequency (%)
e	1142	11.7%
i	1032	10.6%
	987	10.1%
a	872	9.0%
r	806	8.3%
n	680	7.0%
g	432	4.4%
m	333	3.4%
l	308	3.2%
u	302	3.1%
Other values (38)	2832	29.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7126	73.3%
Uppercase Letter	1448	14.9%
Space Separator	987	10.1%
Decimal Number	96	1.0%
Other Punctuation	69	0.7%

Most frequent character per category

Value	Count	Frequency (%)
e	1142	16.0%
i	1032	14.5%
a	872	12.2%
r	806	11.3%
n	680	9.5%
g	432	6.1%
m	333	4.7%
l	308	4.3%
u	302	4.2%
d	218	3.1%
Other values (14)	1001	14.0%

Value	Count	Frequency (%)
P	293	20.2%
S	235	16.2%
L	230	15.9%
E	143	9.9%
D	116	8.0%
A	91	6.3%
I	81	5.6%
B	70	4.8%
G	69	4.8%
F	33	2.3%
Other values (10)	87	6.0%

Value	Count	Frequency (%)
.	65	94.2%
'	4	5.8%

Value	Count	Frequency (%)
	987	100.0%

Value	Count	Frequency (%)
1	96	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8574	88.2%
Common	1152	11.8%

Most frequent character per script

Value	Count	Frequency (%)
e	1142	13.3%
i	1032	12.0%
a	872	10.2%
r	806	9.4%
n	680	7.9%
g	432	5.0%
m	333	3.9%
l	308	3.6%
u	302	3.5%
P	293	3.4%
Other values (34)	2374	27.7%

Value	Count	Frequency (%)
	987	85.7%
1	96	8.3%
.	65	5.6%
'	4	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9602	98.7%
None	124	1.3%

Most frequent character per block

Value	Count	Frequency (%)
e	1142	11.9%
i	1032	10.7%
	987	10.3%
a	872	9.1%
r	806	8.4%
n	680	7.1%
g	432	4.5%
m	333	3.5%
l	308	3.2%
u	302	3.1%
Other values (36)	2708	28.2%

Value	Count	Frequency (%)
ó	116	93.5%
ü	8	6.5%

Nation
Categorical

HIGH CARDINALITY

Distinct	58
Distinct (%)	11.6%
Missing	0
Missing (%)	0.0%
Memory size	7.8 KiB

Spain	82
Brazil	59
France	55
Germany	35
Argentina	27
Other values (53)	242

Length

Max length	24
Median length	6
Mean length	6.714
Min length	4

Characters and Unicode

Total characters	3357
Distinct characters	47
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	22 ?
Unique (%)	4.4%

Sample

1st row	Argentina
2nd row	Portugal
3rd row	Brazil
4th row	Belgium
5th row	Poland

Value	Count	Frequency (%)
Spain	82	16.4%
Brazil	59	11.8%
France	55	11.0%
Germany	35	7.0%
Argentina	27	5.4%
Portugal	27	5.4%
England	24	4.8%
Italy	23	4.6%
Holland	15	3.0%
Belgium	15	3.0%
Other values (48)	138	27.6%

Histogram of lengths of the category

Value	Count	Frequency (%)
spain	82	15.7%
brazil	59	11.3%
france	55	10.6%
germany	35	6.7%
portugal	27	5.2%
argentina	27	5.2%
england	24	4.6%
italy	23	4.4%
belgium	15	2.9%
holland	15	2.9%
Other values (57)	159	30.5%

Most occurring characters

Value	Count	Frequency (%)
a	480	14.3%
n	347	10.3%
r	289	8.6%
i	267	8.0%
e	233	6.9%
l	228	6.8%
g	122	3.6%
o	118	3.5%
S	107	3.2%
t	106	3.2%
Other values (37)	1060	31.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2813	83.8%
Uppercase Letter	520	15.5%
Space Separator	21	0.6%
Other Punctuation	3	0.1%

Most frequent character per category

Value	Count	Frequency (%)
a	480	17.1%
n	347	12.3%
r	289	10.3%
i	267	9.5%
e	233	8.3%
l	228	8.1%
g	122	4.3%
o	118	4.2%
t	106	3.8%
p	88	3.1%
Other values (15)	535	19.0%

Value	Count	Frequency (%)
S	107	20.6%
B	77	14.8%
F	55	10.6%
G	42	8.1%
A	39	7.5%
P	35	6.7%
C	33	6.3%
I	29	5.6%
E	25	4.8%
H	19	3.7%
Other values (10)	59	11.3%

Value	Count	Frequency (%)
	21	100.0%

Value	Count	Frequency (%)
'	3	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	3333	99.3%
Common	24	0.7%

Most frequent character per script

Value	Count	Frequency (%)
a	480	14.4%
n	347	10.4%
r	289	8.7%
i	267	8.0%
e	233	7.0%
l	228	6.8%
g	122	3.7%
o	118	3.5%
S	107	3.2%
t	106	3.2%
Other values (35)	1036	31.1%

Value	Count	Frequency (%)
	21	87.5%
'	3	12.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3354	99.9%
None	3	0.1%

Most frequent character per block

Value	Count	Frequency (%)
a	480	14.3%
n	347	10.3%
r	289	8.6%
i	267	8.0%
e	233	6.9%
l	228	6.8%
g	122	3.6%
o	118	3.5%
S	107	3.2%
t	106	3.2%
Other values (36)	1057	31.5%

Value	Count	Frequency (%)
ô	3	100.0%

Age
Real number (ℝ_≥0)

Distinct	20
Distinct (%)	4.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	27.972
Minimum	19
Maximum	39
Zeros	0
Zeros (%)	0.0%
Memory size	7.8 KiB

Quantile statistics

Minimum	19
5-th percentile	22
Q1	25
median	28
Q3	31
95-th percentile	34
Maximum	39
Range	20
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	3.783628906
Coefficient of variation (CV)	0.1352648686
Kurtosis	-0.4647576836
Mean	27.972
Median Absolute Deviation (MAD)	3
Skewness	0.130311656
Sum	13986
Variance	14.3158477
Monotocity	Not monotonic

Histogram with fixed size bins (bins=20)

Value	Count	Frequency (%)
29	53	10.6%
27	52	10.4%
26	45	9.0%
28	44	8.8%
25	39	7.8%
32	38	7.6%
23	37	7.4%
31	35	7.0%
30	33	6.6%
33	30	6.0%
Other values (10)	94	18.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
19	1	0.2%
20	7	1.4%
21	8	1.6%
22	17	3.4%
23	37	7.4%

Value	Count	Frequency (%)
39	2	0.4%
37	4	0.8%
36	6	1.2%
35	7	1.4%
34	14	2.8%

Height
Real number (ℝ_≥0)

Distinct	33
Distinct (%)	6.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	180.746
Minimum	163
Maximum	197
Zeros	0
Zeros (%)	0.0%
Memory size	7.8 KiB

Quantile statistics

Minimum	163
5-th percentile	170
Q1	176
median	181
Q3	186
95-th percentile	191
Maximum	197
Range	34
Interquartile range (IQR)	10

Descriptive statistics

Standard deviation	6.563203126
Coefficient of variation (CV)	0.03631174757
Kurtosis	-0.5252059928
Mean	180.746
Median Absolute Deviation (MAD)	5
Skewness	-0.02549150611
Sum	90373
Variance	43.07563527
Monotocity	Not monotonic

Histogram with fixed size bins (bins=33)

Value	Count	Frequency (%)
180	39	7.8%
175	33	6.6%
178	28	5.6%
181	26	5.2%
187	25	5.0%
185	25	5.0%
182	25	5.0%
184	23	4.6%
183	23	4.6%
186	22	4.4%
Other values (23)	231	46.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
163	2	0.4%
165	3	0.6%
167	2	0.4%
168	3	0.6%
169	6	1.2%

Value	Count	Frequency (%)
197	1	0.2%
196	1	0.2%
195	5	1.0%
194	7	1.4%
193	2	0.4%

Position
Categorical

Distinct	14
Distinct (%)	2.8%
Missing	0
Missing (%)	0.0%
Memory size	7.8 KiB

CB	80
ST	71
CM	70
CDM	46
CAM	41
Other values (9)	192

Length

Max length	3
Median length	2
Mean length	2.184
Min length	2

Characters and Unicode

Total characters	1092
Distinct characters	11
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.2%

Sample

1st row	RW
2nd row	ST
3rd row	LW
4th row	CAM
5th row	ST

Value	Count	Frequency (%)
CB	80	16.0%
ST	71	14.2%
CM	70	14.0%
CDM	46	9.2%
CAM	41	8.2%
LB	37	7.4%
RB	36	7.2%
LM	31	6.2%
RM	30	6.0%
RW	22	4.4%
Other values (4)	36	7.2%

Histogram of lengths of the category

Value	Count	Frequency (%)
cb	80	16.0%
st	71	14.2%
cm	70	14.0%
cdm	46	9.2%
cam	41	8.2%
lb	37	7.4%
rb	36	7.2%
lm	31	6.2%
rm	30	6.0%
rw	22	4.4%
Other values (4)	36	7.2%

Most occurring characters

Value	Count	Frequency (%)
C	248	22.7%
M	218	20.0%
B	158	14.5%
R	92	8.4%
L	89	8.2%
S	71	6.5%
T	71	6.5%
W	47	4.3%
D	46	4.2%
A	41	3.8%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	1092	100.0%

Most frequent character per category

Value	Count	Frequency (%)
C	248	22.7%
M	218	20.0%
B	158	14.5%
R	92	8.4%
L	89	8.2%
S	71	6.5%
T	71	6.5%
W	47	4.3%
D	46	4.2%
A	41	3.8%

Most occurring scripts

Value	Count	Frequency (%)
Latin	1092	100.0%

Most frequent character per script

Value	Count	Frequency (%)
C	248	22.7%
M	218	20.0%
B	158	14.5%
R	92	8.4%
L	89	8.2%
S	71	6.5%
T	71	6.5%
W	47	4.3%
D	46	4.2%
A	41	3.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1092	100.0%

Most frequent character per block

Value	Count	Frequency (%)
C	248	22.7%
M	218	20.0%
B	158	14.5%
R	92	8.4%
L	89	8.2%
S	71	6.5%
T	71	6.5%
W	47	4.3%
D	46	4.2%
A	41	3.8%

Rating
Real number (ℝ_≥0)

Distinct	15
Distinct (%)	3.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	82.072
Minimum	79
Maximum	93
Zeros	0
Zeros (%)	0.0%
Memory size	7.8 KiB

Quantile statistics

Minimum	79
5-th percentile	79
Q1	80
median	81
Q3	83.25
95-th percentile	87
Maximum	93
Range	14
Interquartile range (IQR)	3.25

Descriptive statistics

Standard deviation	2.626139604
Coefficient of variation (CV)	0.03199799693
Kurtosis	1.274085037
Mean	82.072
Median Absolute Deviation (MAD)	2
Skewness	1.1445322
Sum	41036
Variance	6.896609218
Monotocity	Decreasing

Histogram with fixed size bins (bins=15)

Value	Count	Frequency (%)
80	118	23.6%
81	84	16.8%
83	68	13.6%
79	61	12.2%
82	44	8.8%
84	41	8.2%
85	34	6.8%
87	17	3.4%
86	13	2.6%
88	7	1.4%
Other values (5)	13	2.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
79	61	12.2%
80	118	23.6%
81	84	16.8%
82	44	8.8%
83	68	13.6%

Value	Count	Frequency (%)
93	1	0.2%
92	1	0.2%
91	3	0.6%
90	4	0.8%
89	4	0.8%

PACE
Real number (ℝ_≥0)

Distinct	53
Distinct (%)	10.7%
Missing	3
Missing (%)	0.6%
Infinite	0
Infinite (%)	0.0%

Mean	74.6277666
Minimum	37
Maximum	96
Zeros	0
Zeros (%)	0.0%
Memory size	7.8 KiB

Quantile statistics

Minimum	37
5-th percentile	57
Q1	67
median	75
Q3	82
95-th percentile	92
Maximum	96
Range	59
Interquartile range (IQR)	15

Descriptive statistics

Standard deviation	10.77109116
Coefficient of variation (CV)	0.1443308792
Kurtosis	0.02703034591
Mean	74.6277666
Median Absolute Deviation (MAD)	8
Skewness	-0.3730775261
Sum	37090
Variance	116.0164049
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
68	23	4.6%
78	21	4.2%
73	19	3.8%
81	19	3.8%
80	19	3.8%
75	18	3.6%
83	17	3.4%
74	17	3.4%
67	17	3.4%
72	16	3.2%
Other values (43)	311	62.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
37	1	0.2%
42	1	0.2%
43	1	0.2%
44	1	0.2%
45	3	0.6%

Value	Count	Frequency (%)
96	2	0.4%
95	1	0.2%
94	6	1.2%
93	10	2.0%
92	8	1.6%

SHOOTING
Real number (ℝ_≥0)

Distinct	63
Distinct (%)	12.7%
Missing	3
Missing (%)	0.6%
Infinite	0
Infinite (%)	0.0%

Mean	69.5331992
Minimum	25
Maximum	93
Zeros	0
Zeros (%)	0.0%
Memory size	7.8 KiB

Quantile statistics

Minimum	25
5-th percentile	42.8
Q1	63
median	74
Q3	79
95-th percentile	85
Maximum	93
Range	68
Interquartile range (IQR)	16

Descriptive statistics

Standard deviation	13.07251042
Coefficient of variation (CV)	0.1880038682
Kurtosis	0.4276782674
Mean	69.5331992
Median Absolute Deviation (MAD)	7
Skewness	-1.00789575
Sum	34558
Variance	170.8905287
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
75	32	6.4%
80	27	5.4%
79	24	4.8%
74	24	4.8%
76	24	4.8%
78	22	4.4%
82	19	3.8%
81	17	3.4%
77	17	3.4%
73	17	3.4%
Other values (53)	274	54.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
25	1	0.2%
27	1	0.2%
28	1	0.2%
30	1	0.2%
32	1	0.2%

Value	Count	Frequency (%)
93	1	0.2%
92	1	0.2%
91	2	0.4%
90	2	0.4%
88	2	0.4%

PASSING
Real number (ℝ_≥0)

Distinct	42
Distinct (%)	8.5%
Missing	3
Missing (%)	0.6%
Infinite	0
Infinite (%)	0.0%

Mean	74.74245473
Minimum	42
Maximum	93
Zeros	0
Zeros (%)	0.0%
Memory size	7.8 KiB

Quantile statistics

Minimum	42
5-th percentile	58.8
Q1	71
median	76
Q3	80
95-th percentile	84.2
Maximum	93
Range	51
Interquartile range (IQR)	9

Descriptive statistics

Standard deviation	7.651564541
Coefficient of variation (CV)	0.1023724009
Kurtosis	1.023398646
Mean	74.74245473
Median Absolute Deviation (MAD)	4
Skewness	-0.9441798606
Sum	37147
Variance	58.54643993
Monotocity	Not monotonic

Histogram with fixed size bins (bins=42)

Value	Count	Frequency (%)
78	39	7.8%
79	33	6.6%
80	33	6.6%
76	32	6.4%
77	31	6.2%
81	31	6.2%
72	29	5.8%
74	27	5.4%
75	23	4.6%
82	20	4.0%
Other values (32)	199	39.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
42	1	0.2%
52	3	0.6%
53	1	0.2%
54	4	0.8%
55	4	0.8%

Value	Count	Frequency (%)
93	1	0.2%
91	2	0.4%
90	1	0.2%
89	1	0.2%
88	3	0.6%

DRIBBLING
Real number (ℝ_≥0)

Distinct	41
Distinct (%)	8.2%
Missing	3
Missing (%)	0.6%
Infinite	0
Infinite (%)	0.0%

Mean	78.42052314
Minimum	51
Maximum	95
Zeros	0
Zeros (%)	0.0%
Memory size	7.8 KiB

Quantile statistics

Minimum	51
5-th percentile	62
Q1	75
median	80
Q3	83
95-th percentile	88
Maximum	95
Range	44
Interquartile range (IQR)	8

Descriptive statistics

Standard deviation	7.511967691
Coefficient of variation (CV)	0.09579083881
Kurtosis	0.8174580184
Mean	78.42052314
Median Absolute Deviation (MAD)	4
Skewness	-0.9367116343
Sum	38975
Variance	56.4296586
Monotocity	Not monotonic

Histogram with fixed size bins (bins=41)

Value	Count	Frequency (%)
82	42	8.4%
80	38	7.6%
83	34	6.8%
81	33	6.6%
78	31	6.2%
85	29	5.8%
84	23	4.6%
79	22	4.4%
87	21	4.2%
75	20	4.0%
Other values (31)	204	40.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
51	1	0.2%
55	1	0.2%
56	4	0.8%
57	1	0.2%
58	1	0.2%

Value	Count	Frequency (%)
95	1	0.2%
94	1	0.2%
92	2	0.4%
91	4	0.8%
90	10	2.0%

DEFENCE
Real number (ℝ_≥0)

Distinct	65
Distinct (%)	13.1%
Missing	3
Missing (%)	0.6%
Infinite	0
Infinite (%)	0.0%

Mean	63.25754527
Minimum	24
Maximum	91
Zeros	0
Zeros (%)	0.0%
Memory size	7.8 KiB

Quantile statistics

Minimum	24
5-th percentile	31
Q1	44
median	72
Q3	80
95-th percentile	85
Maximum	91
Range	67
Interquartile range (IQR)	36

Descriptive statistics

Standard deviation	18.92967856
Coefficient of variation (CV)	0.2992477573
Kurtosis	-1.326804857
Mean	63.25754527
Median Absolute Deviation (MAD)	12
Skewness	-0.4534566285
Sum	31439
Variance	358.3327303
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
77	28	5.6%
80	26	5.2%
82	22	4.4%
79	21	4.2%
78	20	4.0%
81	18	3.6%
43	17	3.4%
75	15	3.0%
83	15	3.0%
39	14	2.8%
Other values (55)	301	60.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
24	2	0.4%
25	2	0.4%
27	3	0.6%
29	4	0.8%
30	10	2.0%

Value	Count	Frequency (%)
91	1	0.2%
90	1	0.2%
89	2	0.4%
88	2	0.4%
87	8	1.6%

PHYSICAL
Real number (ℝ_≥0)

Distinct	41
Distinct (%)	8.2%
Missing	3
Missing (%)	0.6%
Infinite	0
Infinite (%)	0.0%

Mean	73.22334004
Minimum	44
Maximum	91
Zeros	0
Zeros (%)	0.0%
Memory size	7.8 KiB

Quantile statistics

Minimum	44
5-th percentile	58
Q1	68
median	75
Q3	80
95-th percentile	85
Maximum	91
Range	47
Interquartile range (IQR)	12

Descriptive statistics

Standard deviation	8.315624777
Coefficient of variation (CV)	0.1135652208
Kurtosis	-0.1550847136
Mean	73.22334004
Median Absolute Deviation (MAD)	6
Skewness	-0.6166689679
Sum	36392
Variance	69.14961543
Monotocity	Not monotonic

Histogram with fixed size bins (bins=41)

Value	Count	Frequency (%)
77	29	5.8%
76	29	5.8%
79	26	5.2%
78	25	5.0%
80	24	4.8%
81	24	4.8%
73	21	4.2%
83	20	4.0%
75	20	4.0%
69	20	4.0%
Other values (31)	259	51.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
44	1	0.2%
50	2	0.4%
51	1	0.2%
52	2	0.4%
53	3	0.6%

Value	Count	Frequency (%)
91	1	0.2%
88	2	0.4%
87	1	0.2%
86	8	1.6%
85	15	3.0%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	Name	Club	League	Nation	Age	Height	Position	Rating	PACE	SHOOTING	PASSING	DRIBBLING	DEFENCE	PHYSICAL
0	Lionel Messi	FC Barcelona	Spain Primera División	Argentina	33	170	RW	93	85.0	92.0	91.0	95.0	38.0	65.0
1	C. Ronaldo dos Santos Aveiro	Piemonte Calcio	Italy Serie A	Portugal	35	187	ST	92	89.0	93.0	81.0	89.0	35.0	77.0
2	Neymar da Silva Santos Jr.	Paris Saint-Germain	France Ligue 1	Brazil	28	175	LW	91	91.0	85.0	86.0	94.0	36.0	59.0
3	Kevin De Bruyne	Manchester City	England Premier League	Belgium	29	181	CAM	91	76.0	86.0	93.0	88.0	64.0	78.0
4	Robert Lewandowski	Bayern München	Germany 1. Bundesliga	Poland	32	184	ST	91	78.0	91.0	78.0	86.0	43.0	82.0
5	Sadio Mané	Liverpool	England Premier League	Senegal	28	175	LW	90	94.0	85.0	80.0	90.0	44.0	76.0
6	Mohamed Salah	Liverpool	England Premier League	Egypt	28	175	RW	90	93.0	86.0	81.0	90.0	45.0	75.0
7	Kylian Mbappé	Paris Saint-Germain	France Ligue 1	France	21	178	ST	90	96.0	86.0	78.0	91.0	39.0	76.0
8	Virgil van Dijk	Liverpool	England Premier League	Holland	29	193	CB	90	76.0	60.0	71.0	72.0	91.0	86.0
9	Carlos Henrique Venancio Casimiro	Real Madrid	Spain Primera División	Brazil	28	185	CDM	89	65.0	73.0	76.0	73.0	86.0	91.0

Last rows

	Name	Club	League	Nation	Age	Height	Position	Rating	PACE	SHOOTING	PASSING	DRIBBLING	DEFENCE	PHYSICAL
490	Jaume Vicent Costa Jordá	Villarreal CF	Spain Primera División	Spain	32	171	LB	79	75.0	61.0	72.0	73.0	78.0	70.0
491	Nicolás Otamendi	Manchester City	England Premier League	Argentina	32	183	CB	79	54.0	57.0	61.0	59.0	80.0	78.0
492	Chris Smalling	Manchester United	England Premier League	England	30	194	CB	79	68.0	47.0	58.0	59.0	81.0	81.0
493	Diego Perotti	Generic Capitale	Italy Serie A	Argentina	32	179	LM	79	74.0	71.0	77.0	85.0	42.0	54.0
494	Marcelo Díaz	Racing Club de Avellaneda	CONMEBOL Libertadores	Chile	33	167	CDM	79	59.0	66.0	80.0	75.0	74.0	74.0
495	Kévin Gameiro	Valencia CF	Spain Primera División	France	33	172	ST	79	86.0	79.0	74.0	76.0	43.0	67.0
496	Sokratis Papastathopoulos	Arsenal	England Premier League	Greece	32	186	CB	79	67.0	53.0	52.0	60.0	78.0	81.0
497	Daniel Olmo Carvajal	RB Leipzig	Germany 1. Bundesliga	Spain	22	179	CAM	79	68.0	76.0	77.0	82.0	50.0	62.0
498	Marcus Wendel Valle da Silva	Sporting CP	Portugal Primeira Liga	Brazil	23	180	CM	79	78.0	75.0	77.0	80.0	73.0	71.0
499	Igor Zubeldia Elorza	Real Sociedad	Spain Primera División	Spain	23	181	CDM	79	65.0	67.0	76.0	72.0	75.0	80.0

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Interactions

Correlations

Pearson's r

Spearman's ρ

Kendall's τ

Phik (φk)

Cramér's V (φc)

Missing values

Sample

First rows

Last rows